Certified AI Infrastructure Architect (C-AIIA) Certification Program by Tonex
Build the expertise to design, scale, and operate enterprise AI platforms across cloud, hybrid, and on-prem environments. This program covers GPU/TPU clusters, distributed training frameworks, storage tiers, and high-speed networking—optimized for performance, cost, and reliability. You learn how to size workloads, choose accelerators, and architect data pipelines that sustain iterative model development and continuous delivery. We emphasize observability and SRE practices so platforms stay healthy under real-world load.
Cybersecurity is woven throughout. You will map threats across the AI supply chain, secure containers and artifacts, protect data in motion and at rest, and enforce zero-trust controls around model training and inference. The program also addresses FinOps and sustainability. You apply utilization analytics, budget controls, carbon-aware scheduling, and procurement strategies to reduce spend and footprint without sacrificing throughput. By the end, you will be ready to produce reference architectures, migration roadmaps, and governance guardrails that align AI infrastructure with business goals and risk posture.
Learning Objectives:
- Design cross-platform AI/ML architectures for cloud, hybrid, and on-prem.
- Select and orchestrate GPUs/TPUs for training and serving.
- Build resilient data and storage layers for AI pipelines.
- Tune performance for distributed training at scale.
- Implement observability, SRE, and incident response.
- Apply security, compliance, and zero-trust controls.
- Optimize cost and sustainability for AI workloads.
Audience:
- Cloud and Platform Architects
- MLOps and DevOps Engineers
- Data Engineers and AI Infra Leads
- Network and Systems Architects
- Site Reliability Engineers (SRE)
- Cybersecurity Professionals
- IT Managers and Technology Leaders
- FinOps and Compliance Officers
Program Modules:
Module 1: AI/ML Infrastructure Foundations
- Reference patterns for cloud, hybrid, and on-prem
- Workload profiling and capacity sizing
- Accelerator options: GPU, TPU, DPU, IPU
- Storage tiers: NVMe, object, parallel file systems
- Network topologies: InfiniBand, RoCE, Ethernet
- Security baseline and threat modeling for AI stacks
Module 2: Compute Orchestration and Distributed Training
- Kubernetes and Slurm scheduling strategies
- Ray, Horovod, and DDP orchestration patterns
- Container images, registries, and hardening
- GPU/TPU quota, isolation, and time-slicing
- Multi-tenancy, QoS, and resource fairness
- Autoscaling for training and real-time serving
Module 3: Data and Storage Architecture
- Ingestion pipelines and feature stores
- Data lineage, cataloging, and governance
- Object vs. parallel FS for training workloads
- Caching, tiering, and locality optimization
- Backup, DR, and immutability approaches
- Encryption, tokenization, and access controls
Module 4: Networking, Performance, and Observability
- High-speed fabrics, PCIe and NUMA awareness
- NCCL, RDMA, and collective comms tuning
- Profiling kernels and end-to-end bottlenecks
- Telemetry with Prometheus and OpenTelemetry
- Capacity planning, benchmarks, and SLIs
- Troubleshooting playbooks and escalation paths
Module 5: Reliability, Compliance, and Security
- Zero-trust segmentation and secrets management
- Supply chain security for models and containers
- Model registry policy, provenance, and rollback
- Policy-as-code and regulatory mapping
- Incident response for AI workloads
- Business continuity for critical AI services
Module 6: FinOps and Sustainable AI
- TCO modeling and budget guardrails
- Utilization dashboards and chargeback/showback
- Spot and preemptible strategies at scale
- Carbon-aware scheduling and energy profiles
- Vendor neutrality and procurement strategy
- Roadmaps and migration playbooks
Exam Domains:
- Enterprise AI Systems Strategy and Governance
- Accelerator-Aware Cluster Design and Orchestration
- Data Lifecycle, Compliance, and Protection
- High-Performance Networking and Distributed Training
- Observability, SRE, and Incident Readiness
- FinOps, Capacity Economics, and Sustainability
Course Delivery:
The course is delivered through lectures, interactive discussions, workshops, and project-based learning, facilitated by experts in the field of Certified AI Infrastructure Architect (C-AIIA). Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification:
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified AI Infrastructure Architect (C-AIIA).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified AI Infrastructure Architect (C-AIIA) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to architect world-class AI platforms? Enroll today. Drive performance, security, and value—without the waste.